Multi Model Implementation on General Medicine Prediction with Quantum Neural Networks

Kumar, Swarn Avinash; Kumar, Abhishek; Dutt, Vishal; Agrawal, Rashmi

doi:10.1109/icicv50876.2021.9388575

Cited by 48 publications

(4 citation statements)

References 16 publications

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“…In [2], Swarn Kumar et al offered a technique with QNN that can forecast the precise general medicine for the individual with any illness characteristics as the signs of disease. Many patients who do not have access to higher dosage medications right away will bene t from drug prognosis in general medicine using diverse combinations.…”

Section: Literature Surveymentioning

confidence: 99%

A Hybrid Blended Stacking Disease Prediction System Based on Symptoms

Indupalli

Pradeepini

2023

Preprint

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With the pandemic resolution especially from the second wave, many people are suffering from post-COVID symptoms. People need to identify their diseases at an early stage to prevent or save their life. The proposed system designs an automated system that can predict the disease based on the symptoms passed by the people. Since the symptoms-based disease dataset contains categorical data previous researchers utilized boosting algorithms “CatBoost” (Categorical Boosting) because they require lesser resources but their performance in terms of accuracy is "79.4"%. The proposed model blends the ensemble boosting algorithm with the traditional naive Bayesian algorithm to improve the support and accuracy of the model. Blending a pure ensemble algorithm is raising the problem of overfitting, so it blends the combination of non-linear models with the ensemble algorithm. One of the blending layers is implemented as XGBOOST because it assigns the weights to the symptoms based on the disease because every symptom will not have the same impact on all the diseases. During the process of blending, the decision of XGBOOST plays an important role. The proposed model's accuracy of "97.2%" is significantly higher than the single-boost strategy.

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Section: Literature Surveymentioning

confidence: 99%

A Hybrid Blended Stacking Disease Prediction System Based on Symptoms

Indupalli

Pradeepini

2023

Preprint

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“…For the evaluation, a week-ahead sales prediction was done using three years of daily sales, which were grouped weekly. In another study, researchers used Quantum Neural Networks-QNN [25] to predict the exact generic medicine for a patient by considering disease factors [26]. For that, initially, the symptoms of disease (105 symptoms) were categorized by experts.…”

Section: Intrductionmentioning

confidence: 99%

Predicting the Pharmaceutical Needs of Hospitals

Nabizadeh,

Ghaemi,

Goncalves

2023

Preprint

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Purpose: People’s lives are always threatened by various diseases. The role of health and medical services, in particular medicine, is undeniable in protecting their lives. Timely preparation and providing medicine for patients is vital since medicine shortage can endanger their lives while excessive accumulation of medicine can put them at expiration risk and waste health budgets. In this paper, we introduce a model for the prediction of commonly used medicine (type and amount) in hospitals. Methods: We have used a dataset of Afzalipur Hospital in Kerman collected for three years consisting of 283 features, which included over 12293431 medicine and 9531 patients. Nine features were selected using experts’ feedback and were fed into the random forest and neural network algorithms. For the prediction task, medicine types and their amounts were predicted for each individual using different training sets. In addition, the right prediction time was also found which is when predictions have a promising accuracy while the executive team of a hospital has enough time to provide the right amounts of the most used medicine. Results: The performance of algorithms was evaluated using a confusion matrix. Our results showed that the random forest had a promising performance in predicting the amounts of the most used medicine for a month using two years of data (accuracy 83.3%) while its accuracy in predicting medicine was 35.9%. Conclusion: Although our results are promising, in particular, for predicting the amounts of medicine, these results could be enhanced and more reliable using other metadata like ”patient underlying disease” and ”medical tests results”.

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“…The vast potential of quantum computing is poised to drive significant progress in the healthcare sector, offering promising prospects for improving the diagnosis and therapeutic interventions for a multitude of illnesses [118,119]. Quantum computing could be beneficial in this context: Efficient Genetic Analysis: Quantum computers have the capability to process vast amounts of genetic data much faster than classical computers.…”

Section: Quantum Computingmentioning

confidence: 99%

Exploring Huntington’s Disease Diagnosis via Artificial Intelligence Models: A Comprehensive Review

Ganesh,

Chithambaram,

Krishnan

et al. 2023

Diagnostics

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Huntington’s Disease (HD) is a devastating neurodegenerative disorder characterized by progressive motor dysfunction, cognitive impairment, and psychiatric symptoms. The early and accurate diagnosis of HD is crucial for effective intervention and patient care. This comprehensive review provides a comprehensive overview of the utilization of Artificial Intelligence (AI) powered algorithms in the diagnosis of HD. This review systematically analyses the existing literature to identify key trends, methodologies, and challenges in this emerging field. It also highlights the potential of ML and DL approaches in automating HD diagnosis through the analysis of clinical, genetic, and neuroimaging data. This review also discusses the limitations and ethical considerations associated with these models and suggests future research directions aimed at improving the early detection and management of Huntington’s disease. It also serves as a valuable resource for researchers, clinicians, and healthcare professionals interested in the intersection of machine learning and neurodegenerative disease diagnosis.

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Multi Model Implementation on General Medicine Prediction with Quantum Neural Networks

Cited by 48 publications

References 16 publications

A Hybrid Blended Stacking Disease Prediction System Based on Symptoms

A Hybrid Blended Stacking Disease Prediction System Based on Symptoms

Predicting the Pharmaceutical Needs of Hospitals

Exploring Huntington’s Disease Diagnosis via Artificial Intelligence Models: A Comprehensive Review

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